Chapter 5 Spatial R

We’ll explore the basics of simple features (sf) for building spatial datasets, then some common mapping methods, probably:

  • ggplot2
  • tmap

5.1 Spatial Data

To work with spatial data requires extending R to deal with it using packages. Many have been developed, but the field is starting to mature using international open GIS standards.

sp (until recently, the dominant library of spatial tools)

  • Includes functions for working with spatial data
  • Includes spplot to create maps
  • Also needs rgdal package for readOGR – reads spatial data frames.

sf (Simple Features)

  • ISO 19125 standard for GIS geometries
  • Also has functions for working with spatial data, but clearer to use.
  • Doesn’t need many additional packages, though you may still need rgdal installed for some tools you want to use.
  • Replacing sp and spplot though you’ll still find them in code. We’ll give it a try…
  • Works with ggplot2 and tmap for nice looking maps.

Cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/sf.pdf

5.1.0.1 simple feature geometry sfg and simple feature column sfc

5.1.1 Examples of simple geometry building in sf

sf functions have the pattern st_*

st means “space and time”

See Geocomputation with R at https://geocompr.robinlovelace.net/ or https://r-spatial.github.io/sf/ for more details, but here’s an example of manual feature creation of sf geometries (sfg):

library(tidyverse)
library(sf)
library(sf)
eyes <- st_multipoint(rbind(c(1,5), c(3,5)))
nose <- st_point(c(2,4))
mouth <- st_linestring(rbind(c(1,3),c(3, 3)))
border <- st_polygon(list(rbind(c(0,5), c(1,2), c(2,1), c(3,2), 
                              c(4,5), c(3,7), c(1,7), c(0,5))))
face <- st_sfc(eyes, nose, mouth, border)  # sfc = sf column 
plot(face)
Building simple geometries in sf

Figure 5.1: Building simple geometries in sf

The face was a simple feature column (sfc) can be built from the list of sfgs. An sfc just has the one column, so not quite like a shapefile.

  • But it can have a coordinate referencing system CRS, and so can be mapped.
  • Kind of like a shapefile with no other attributes than shape

5.1.2 Building a mappable sfc from scratch

CA_matrix <- rbind(c(-124,42),c(-120,42),c(-120,39),c(-114.5,35),
  c(-114.1,34.3),c(-114.6,32.7),c(-117,32.5),c(-118.5,34),c(-120.5,34.5),
  c(-122,36.5),c(-121.8,36.8),c(-122,37),c(-122.4,37.3),c(-122.5,37.8),
  c(-123,38),c(-123.7,39),c(-124,40),c(-124.4,40.5),c(-124,41),c(-124,42))
NV_matrix <- rbind(c(-120,42),c(-114,42),c(-114,36),c(-114.5,36),
  c(-114.5,35),c(-120,39),c(-120,42))
CA_list <- list(CA_matrix);       NV_list <- list(NV_matrix)
CA_poly <- st_polygon(CA_list);   NV_poly <- st_polygon(NV_list)
sfc_2states <- st_sfc(CA_poly,NV_poly,crs=4326)  # crs=4326 specifies GCS
st_geometry_type(sfc_2states)
## [1] POLYGON POLYGON
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
library(tidyverse)
ggplot() + geom_sf(data = sfc_2states)
A simple map built from scratch with hard-coded data as simple feature columns

Figure 5.2: A simple map built from scratch with hard-coded data as simple feature columns

sf class

Is like a shapefile: has attributes to which geometry is added, and can be used like a data frame.

attributes <- bind_rows(c(abb="CA", area=423970, pop=39.56e6),
                        c(abb="NV", area=286382, pop=3.03e6))
twostates <- st_sf(attributes, geometry = sfc_2states)
ggplot(twostates) + geom_sf() + geom_sf_text(aes(label = abb))
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Using an sf class to build a map, displaying an attribute

Figure 5.3: Using an sf class to build a map, displaying an attribute

5.1.3 Creating features from shapefiles or tables

sf’s st_read reads shapefiles

  • shapefile is an open GIS format for points, polylines, polygons

st_as_sf converts data frames

  • using coordinates in data
library(tidyverse)
library(sf)
co <- st_read("data/BayAreaCounties.shp")
## Reading layer `BayAreaCounties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaCounties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 60 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## geographic CRS: WGS 84
freeways <- st_read("data/CAfreeways.shp")
## Reading layer `CAfreeways' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\CAfreeways.shp' using driver `ESRI Shapefile'
## Simple feature collection with 72 features and 6 fields
## geometry type:  MULTILINESTRING
## dimension:      XY
## bbox:           xmin: -124.2162 ymin: 32.54233 xmax: -114.4879 ymax: 42.00536
## geographic CRS: WGS 84
censusBayArea <- st_read("data/BayAreaTracts.shp")
## Reading layer `BayAreaTracts' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaTracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1663 features and 50 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## geographic CRS: WGS 84
censusCentroids <- st_centroid(censusBayArea)
TRIdata <- read_csv("data/TRI_2017_CA.csv")
TRI_sp <- st_as_sf(TRIdata, coords = c("LONGITUDE", "LATITUDE"), 
        crs=4326) # simple way to specify coordinate reference
bnd <- st_bbox(censusCentroids)
ggplot() +
  geom_sf(data = co, aes(fill = NAME)) +
  geom_sf(data = censusCentroids) +
  geom_sf(data = freeways, color = "grey") +
  geom_sf(data = TRI_sp, color = "yellow") +
  coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
  labs(title="Bay Area Counties, Freeways and Census Tract Centroids")
ggplot map of Bay Area TRI sites, census centroids, freeways

Figure 5.4: ggplot map of Bay Area TRI sites, census centroids, freeways

5.1.4 Coordinate Referencing System

Say you have data you need to make spatial with a spatial reference

sierra <- read_csv("sierraClimate.csv")

EPSG or CRS codes are an easy way to provide coordinate referencing.

Two ways of doing the same thing.

  1. Spell it out:
GCS <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
wsta = st_as_sf(sierra, coords = c("LONGITUDE","LATITUDE"), crs=GCS)
  1. Google to find the code you need and assign it to the crs parameter:

wsta <- st_as_sf(sierra, coords = c("LONGITUDE","LATITUDE"), crs=4326)

5.1.4.1 Removing Geometry

There are many instances where you want to remove geometry from a sf data frame

  • Some R functions run into problems with geometry and produce confusing error messages, like “non-numeric argument”

  • You’re wanting to work with an sf data frame in a non-spatial way

One way to remove geometry:

myNonSFdf <- mySFdf %>% st_set_geometry(NULL)

5.1.5 Spatial join st_join

A spatial join with st_join joins data from census where TRI points occur

TRIdata <- read_csv("data/TRI_2017_CA.csv")
## Warning: Missing column names filled in: 'X110' [110]
## Warning: 3807 parsing failures.
## row col    expected      actual                   file
##   1  -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
##   2  -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
##   3  -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
##   4  -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
##   5  -- 110 columns 109 columns 'data/TRI_2017_CA.csv'
## ... ... ........... ........... ......................
## See problems(...) for more details.
TRI_sp <- st_as_sf(TRIdata, coords = c("LONGITUDE", "LATITUDE"), 
        crs=4326) %>%
  st_join(censusBayArea) %>%
  filter(CNTY_FIPS %in% c("013", "095"))

5.1.6 Plotting maps in the base plot system

There are various programs for creating maps from spatial data, and we’ll look at a few after we’ve looked at rasters. As usual, the base plot system often does something useful when you give it data.

co <- st_read("data/BayAreaCounties.shp")
## Reading layer `BayAreaCounties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaCounties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 60 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## geographic CRS: WGS 84
plot(co)
## Warning: plotting the first 9 out of 60 attributes; use max.plot = 60 to plot
## all

And with just one variable:

plot(co["POP_SQMI"])

There’s a lot more we could do with the base plot system, but we’ll mostly focus on some better options in ggplot2 and tmap.

5.2 Raster GIS in R

Simple Features are feature-based, so based on the name I guess it’s not surprising that sf doesn’t have support for rasters. But we can use the raster package for that.

A bit of raster reading and map algebra with Marble Mountains elevation data

library(raster)
elev <- raster("data/elev.tif")
slope <- terrain(elev, opt="slope")
aspect <- terrain(elev, opt="aspect")
slopeclasses <-matrix(c(0,0.2,1, 0.2,0.4,2, 0.4,0.6,3,
                        0.6,0.8,4, 0.8,1,5), ncol=3, byrow=TRUE)
slopeclass <- reclassify(slope, rcl = slopeclasses)

plot(elev)

plot(slope)

plot(slopeclass)

plot(aspect)

5.2.1 Raster from scratch

new_raster2 <- raster(nrows = 6, ncols = 6, res = 0.5,
                      xmn = -1.5, xmx = 1.5, ymn = -1.5, ymx = 1.5,
                      vals = 1:36)
plot(new_raster2)

5.3 ggplot2 for maps

The Grammar of Graphics is the gg of ggplot.

  • Key concept is separating aesthetics from data
  • Aesthetics can come from variables (using aes()setting) or be constant for the graph

Mapping tools that follow this lead

  • ggplot, as we have seen, and it continues to be enhanced
  • tmap (Thematic Maps) https://github.com/mtennekes/tmap Tennekes, M., 2018, tmap: Thematic Maps in R, Journal of Statistical Software 84(6), 1-39
CA <- st_read("data/CA_counties.shp")
## Reading layer `CA_counties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\CA_counties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 58 features and 60 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -124.4152 ymin: 32.53427 xmax: -114.1312 ymax: 42.00952
## geographic CRS: WGS 84
ggplot(CA) + geom_sf()

Try ?geom_sf and you’ll find that its first parameters is mapping with aes() by default. The data property is inherited from the ggplot call, but commonly you’ll want to specify data=something in your geom_sf call.

Another simple ggplot, with labels

CA <- st_read("data/CA_counties.shp")
## Reading layer `CA_counties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\CA_counties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 58 features and 60 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -124.4152 ymin: 32.53427 xmax: -114.1312 ymax: 42.00952
## geographic CRS: WGS 84
ggplot(CA) + geom_sf() +
  geom_sf_text(aes(label = NAME), size = 1.5)
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

and now with fill color

ggplot(CA) + geom_sf(aes(fill = MED_AGE)) +
  geom_sf_text(aes(label = NAME), col="white", size=1.5)
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

Repositioned legend, no “x” or “y” labels

ggplot(CA) + geom_sf(aes(fill=MED_AGE)) +
  geom_sf_text(aes(label = NAME), col="white", size=1.5) +
  theme(legend.position = c(0.8, 0.8)) +
  labs(x="",y="")

Map in ggplot2, zoomed into two counties:

library(tidyverse); library(sf)
co <- st_read("data/BayAreaCounties.shp")
## Reading layer `BayAreaCounties' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaCounties.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 60 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## geographic CRS: WGS 84
census <- st_read("data/BayAreaTracts.shp") %>%
  filter(CNTY_FIPS %in% c("013", "095"))
## Reading layer `BayAreaTracts' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaTracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1663 features and 50 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## geographic CRS: WGS 84
TRI <- read_csv("data/TRI_2017_CA.csv") %>%
  st_as_sf(coords = c("LONGITUDE", "LATITUDE"), crs=4326) %>%
  st_join(census) %>%
  filter(CNTY_FIPS %in% c("013", "095"),
         (`5.1_FUGITIVE_AIR` + `5.2_STACK_AIR`) > 0)
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   TRI_FACILITY_ID = col_character(),
##   FACILITY_NAME = col_character(),
##   STREET_ADDRESS = col_character(),
##   CITY = col_character(),
##   COUNTY = col_character(),
##   ST = col_character(),
##   BIA_CODE = col_logical(),
##   TRIBE = col_logical(),
##   FEDERAL_FACILITY = col_character(),
##   INDUSTRY_SECTOR = col_character(),
##   PRIMARY_SIC = col_logical(),
##   SIC_2 = col_logical(),
##   SIC_3 = col_logical(),
##   SIC_4 = col_logical(),
##   SIC_5 = col_logical(),
##   SIC_6 = col_logical(),
##   CHEMICAL = col_character(),
##   `CAS_#/COMPOUND_ID` = col_character(),
##   CLEAR_AIR_ACT_CHEMICAL = col_character(),
##   CLASSIFICATION = col_character()
##   # ... with 8 more columns
## )
## See spec(...) for full column specifications.
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planar
bnd = st_bbox(census)
ggplot() +
  geom_sf(data = co, aes(fill = NAME)) +
  geom_sf(data = census, color="grey40", fill = NA) +
  geom_sf(data = TRI) +
  coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
  labs(title="Census Tracts and TRI air-release sites") +
  theme(legend.position = "none")

5.3.1 Rasters in ggplot2

Raster display in ggplot2 is currently a little awkward, as are rasters in general in the feature-dominated GIS world.

We can use a trick: converting rasters to a grid of points:

library(tidyverse)
library(sf)
library(raster)
elevras <- raster("data/elev.tif") # note: the filename becomes a variable we'll use for fill
trails <- st_read("data/trails.shp")
## Reading layer `trails' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\trails.shp' using driver `ESRI Shapefile'
## Simple feature collection with 32 features and 8 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: 481903.8 ymin: 4599196 xmax: 486901.9 ymax: 4603200
## projected CRS:  NAD83 / UTM zone 10N
elevpts = as.data.frame(rasterToPoints(elevras))
ggplot() +
  geom_raster(data = elevpts, aes(x = x, y = y, fill = elev)) +
  geom_sf(data = trails)

5.4 tmap

Basic building block is tm_shape(data) followed by various layer elements such as tm_fill() shape can be features or raster See Geocomputation with R Chapter 8 “Making Maps with R” for more information. https://geocompr.robinlovelace.net/adv-map.html

library(spData)
## 
## Attaching package: 'spData'
## The following object is masked _by_ '.GlobalEnv':
## 
##     elev
library(tmap)
tm_shape(world) + tm_fill() + tm_borders()

Color by variable

library(sf)
library(tmap)
census <- st_read("data/BayAreaTracts.shp")
## Reading layer `BayAreaTracts' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\BayAreaTracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1663 features and 50 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.5335 ymin: 36.85053 xmax: -121.2082 ymax: 38.86424
## geographic CRS: WGS 84
tm_shape(census) + tm_fill(col = "MED_AGE")

tmap of sierraFeb with hillshade and point symbols

library(tmap)
library(sf)
library(raster)
tmap_mode("plot")
## tmap mode set to plotting
tmap_options(max.categories = 8)
sierraFeb <- st_read("data/sierraFeb.csv")
## Reading layer `sierraFeb' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\sierraFeb.csv' using driver `CSV'
sierra <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
hillsh <- raster("data/ca_hillsh_WGS84.tif")
bounds <- st_bbox(sierra)
tm_shape(hillsh,bbox=bounds)+
  tm_raster(palette="-Greys",legend.show=FALSE,n=10) + tm_shape(sierra) + tm_symbols(col="TEMPERATURE",
     palette=c("blue","red"), style="cont",n=8) +
  tm_legend() + 
  tm_layout(legend.position=c("RIGHT","TOP"))
## stars object downsampled to 1092 by 915 cells. See tm_shape manual (argument raster.downsample)

Note: “-Greys” needed to avoid negative image, since “Greys” go from light to dark, and to match reflectance as with b&w photography, they need to go from dark to light.

5.4.1 Interactive Maps

The word “static” in “static maps” isn’t something you would have heard in a cartography class 30 years ago, since essentially all maps then were static. Very important in designing maps is considering your audience, and one characteristic of the audience of those maps of yore were that they were printed and thus fixed on paper. A lot of cartographic design relates to that property:

  • Figure-to-ground relationships assume “ground” is a white piece of paper (or possibly a standard white background in a pdf), so good cartographic color schemes tend to range from light for low values to dark for high values.
  • Scale is fixed and there are no “tools” for changing scale, so a lot of attention must be paid to providing scale information.
  • Similarly, without the ability to see the map at different scales, inset maps are often needed to provide context.

Interactive maps change the game in having tools for changing scale, and always being “printed” on a computer or device where the color of the background isn’t necessarily white. We are increasingly used to using interactive maps on our phones or other devices, and often get frustrated not being able to zoom into a static map.

A widely used interactive mapping system is Leaflet, but we’re going to use tmap to access Leaflet behind the scenes and allow us to create maps with one set of commands. The key parameter needed is tmap_mode which must be set to “view” to create an interactive map.

tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(census) + tm_fill(col = "MED_AGE", alpha = 0.5)
library(tmap)
library(sf)
tmap_mode("view")
## tmap mode set to interactive viewing
tmap_options(max.categories = 8)
sierraFeb <- st_read("data/sierraFeb.csv")
## Reading layer `sierraFeb' from data source `C:\Users\900008452\Box\course\604704\R\EnvDataSci\data\sierraFeb.csv' using driver `CSV'
## Warning: no simple feature geometries present: returning a data.frame or tbl_df
sierra <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
hillsh <- raster("data/ca_hillsh_WGS84.tif")
bounds <- st_bbox(sierra)
tm_basemap(leaflet::providers$Esri.NatGeoWorldMap) +
  tm_shape(sierra) + tm_symbols(col="TEMPERATURE",
  palette=c("blue","red"), style="cont",n=8,size=0.2) +
  tm_legend() + 
  tm_layout(legend.position=c("RIGHT","TOP"))
## legend.postion is used for plot mode. Use view.legend.position in tm_view to set the legend position in view mode.
## Warning: Number of levels of the variable "TEMPERATURE" is 53, which
## is larger than max.categories (which is 8), so levels are combined. Set
## tmap_options(max.categories = 53) in the layer function to show all levels.

5.4.1.1 Leaflet

“An open-source JavaScript library for mobile-friendly interactive maps” https://leafletjs.com “The R package leaflet is an interface to the JavaScript library Leaflet to create interactive web maps. It was developed on top of the htmlwidgets framework, which means the maps can be rendered in RMarkdown (v2) documents (which is why you can see it below), Shiny apps, and RStudio IDE / the R console.”

https://blog.rstudio.com/2015/06/24/leaflet-interactive-web-maps-with-r/

https://github.com/rstudio/cheatsheets/blob/master/leaflet.pdf

library(leaflet)
m <- leaflet() %>%
  addTiles() %>%  # default OpenStreetMap tiles
  addMarkers(lng=174.768, lat=-36.852,
             popup="The birthplace of R")
m

5.5 Exercises

  1. Using the method of building simple sf geometries, build a simple 1x1 square object and plot it. Remember that you have to close the polygon, so the first vertex is the same as the last (of 5) vertices.

  2. Build a map in ggplot of Colorado, Wyoming, and Utah with these boundary vertices in GCS. As with the square, remember to close each figure, and assign the crs to what is needed for GCS: 4326.

  • Colorado: \((-109,41),(-102,41),(-102,37),(-109,37)\)
  • Wyoming: \((-111,45),(-104,45),(-104,41),(-111,41)\)
  • Utah: \((-114,42),(-111,42),(-111,41),(-109,41),(-109,37),(-114,37)\)
  • Arizona: \((-114,37),(-109,37),(-109,31.3),(-111,31.3),(-114.8,32.5),(-114.6,32.7),(-114.1,34.3),(-114.5,35),(-114.5,36),(-114,36)\)
  • New Mexico: \((-109,37),(-103,37),(-103,32),(-106.6,32),(-106.5,31.8),(-108.2,31.8),(-108.2,31.3),(-109,31.3)\)

  1. Add in the code for CA and NV and create kind of a western US map…

  2. Create an sf class from the five states adding the fields name, abb, area_sqkm, and population, and create a map labeling with the name.

  • Colorado, CO, 269837, 5758736
  • Wyoming, WY, 253600, 578759
  • Utah, UT, 84899, 3205958
  • Arizona, AZ, 295234, 7278717
  • New Mexico, NM, 314917, 2096829
  • California, CA, 423970, 39368078
  • Nevada, NV, 286382, 3080156
  1. Create a tibble for the highest peaks in the 7 states, with the following names, elevations in m, longitude and latitude, and add them to that map:
  • Wheeler Peak, 4011, -105.4, 36.5
  • Mt. Whitney, 4421, -118.2, 36.5
  • Boundary Peak, 4007, -118.35, 37.9
  • Kings Peak, 4120, -110.3, 40.8
  • Gannett Peak, 4209, -109, 43.2
  • Mt. Elbert, 4401, -106.4, 39.1
  • Humphreys Peak, 3852, -111.7, 35.4

Note: the easiest way to do this is with the tribble function, starting with:

peaks <- tribble(
  ~peak, ~elev, ~longitude, ~latitude,
  "Wheeler Peak", 4011, -105.4, 36.5,

  1. Use a spatial join to add the points to the states to provide a new attribute maximum elevation, and display that using geom_sf_text() with the state polygons.

  2. Even though the result isn’t terribly useful, send that spatially joined data to the base plot system to see what you get.

  3. Read in CA_counties.shp and CAfreeways.shp from the data folder and make a simple map in ggplot, with freeways colored red.

  4. After adding the raster library, create a raster from the built-in volcano matrix of elevations from Auckland’s Maunga Whau Volcano, and use plot() to display it. We’d do more with that dataset but we don’t know what the cell size is.

  5. Use tmap to create a simple map from the SW_States (polygons) and peaksp (points) data we created earlier. Hints: you’ll want to use tm_text with text set to “peak” to label the points, along with the parameter auto.placement=TRUE.

  6. Change the map to the view mode, but don’t use the state borders since the basemap will have them. Just before adding shapes, set the basemap to leaflet::providers$Esri.NatGeoWorldMap, then continue to the peaks after the + to see the peaks on a National Geographic basemap.